Method and internet of things system for gas usage monitoring and warning based on smart gas

ABSTRACT

The present disclosure provides a method and an Internet of Things system for gas usage monitoring and warning based on smart gas, the method is performed by a smart gas safety management platform of an Internet of Things system for gas usage monitoring and warning based on smart gas, comprising: obtaining gas usage data from at least one gas device, based on the gas usage data, determining a gas monitoring object from the at least one gas device; determining initial objects based on the gas monitoring object and a gas usage threshold; processing historical gas data of the initial objects by using an evaluation model to determine suspicions of the initial objects, wherein the evaluation model is a machine learning model; and sending gas usage safety warning information to a gas user corresponding to the target object.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.18/067,731, filed on Dec. 19, 2022, which claims priority of ChinesePatent Application No. 202211452485.9, filed on Nov. 21, 2022, thecontents of each of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to the field of gas safety, and inparticular to a method and Internet of Things system for gas usagemonitoring and warning based on smart gas.

BACKGROUND

During the use of gas, the situation of the gas usage needs to beinspected, monitored, and warned, etc. Usually, most of the gas safetywarnings is used to analyze, judge, and treat gas leakage and abnormalgas pressure. However, the safety hazards of normal gas usage should notbe ignored as well.

Therefore, it is expected to provide a method and Internet of Thingssystem for gas usage monitoring and warning based on smart gas, so as toimprove the safety of gas usage.

SUMMARY

One or more embodiments of the present disclosure provide a method forgas usage monitoring and warning based on smart gas, which is performedby a smart gas safety management platform of an Internet of Thingssystem for gas usage monitoring and warning based on smart gas,comprising: obtaining gas usage data from at least one gas devicethrough a smart gas household device sensing network platform of theInternet of Things system and sending the gas usage data to the smartgas safety management platform, wherein the at least one gas device isconfigured in a smart gas household device object platform of theInternet of Things system; based on the gas usage data, determining agas monitoring object from the at least one gas device; determininginitial objects based on the gas monitoring object and a gas usagethreshold; processing historical gas data of the initial objects byusing an evaluation model to determine suspicions of the initialobjects, wherein the evaluation model is a machine learning model andobtained through joint training; determining a target object based onthe suspicions; and sending gas usage safety warning information to agas user corresponding to the target object.

One of the embodiments of this present disclosure provides an Internetof Things system for gas usage monitoring and warning based on smartgas, wherein the Internet of Things system comprises a smart gas userplatform, a smart gas service platform, a smart gas safety managementplatform, a smart gas household device sensing network platform and asmart gas household device object platform interacted in sequence,wherein the smart gas safety management platform includes a smart gashousehold safety management sub-platform and a smart gas data center,the smart gas data center is configured to: obtain gas usage data fromat least one gas device through the smart gas household device sensingnetwork platform and send the gas usage data to the smart gas householdsafety management sub-platform, wherein the at least one gas device isconfigured in the smart gas household device object platform; the smartgas household safety management sub-platform is configured to: based ongas usage data, determine a gas monitoring object from at least one gasdevice; determine initial objects based on the gas monitoring object anda gas usage threshold; process historical gas data of the initialobjects by using an evaluation model to determine suspicions of theinitial objects, wherein the evaluation model is a machine learningmodel and obtained through joint training; determine a target objectbased on the suspicions; and send the gas safety warning information tothe smart gas data center and to the smart gas user platform from thegas user corresponding to the target object via the smart gas serviceplatform.

One or more embodiments of the present disclosure provide anon-transitory computer-readable storage medium, comprising a set ofinstructions, wherein when executed by a processor, the above-describedmethod for gas usage monitoring and warning based the smart gas isimplemented.

The beneficial effects of embodiments of this present disclosure atleast include:

-   -   (1) Determining a gas device with gas usage safety problems and        sending gas usage safety warning information to a gas user        through gas usage data can examine the safety hazards in the        daily gas usage and send a timely warning to the gas user, which        facilitates the protection of gas usage safety of the gas user        and reduces the probability of gas safety accidents.    -   (2) By receiving feedback information of the gas users, the        accuracy of the gas safety warning information can be verified        and the actual gas usage of the gas user can be understood in a        timely manner. At the same time, the feedback information of the        gas user facilitates the subsequent adjustment of the gas usage        threshold and the suspicion threshold to improve the accuracy of        determining the target object.    -   (3) Determining a gas usage threshold based on the environmental        data can fully consider the impact of different seasons, time        points and areas on gas usage, and thus can make adaptive        adjustments to the gas usage threshold based on different        environments, making the determination of the gas usage        threshold more reasonable and accurate;    -   (4) Based on the feedback information of the user, the count of        warnings, the count of feedback information and the suspicion to        determine the gas usage threshold, the corresponding gas usage        threshold can be flexibly adjusted for the gas usage of        different users, thus making the gas safety warning information        more in line with the actual gas usage of different users and        reducing the risk of untimely warnings.

BRIEF DESCRIPTION OF THE DRAWINGS

This present disclosure will be further illustrated by way of exemplaryembodiments, which will be described in detail by way of theaccompanying drawings. These embodiments are not limiting, and in theseembodiments the same numbering indicates the same structure wherein:

FIG. 1 is a structural diagram illustrating an Internet of Things systemfor gas usage safety warning based on smart gas according to someembodiments of this present disclosure;

FIG. 2 is an exemplary flowchart illustrating a method for gas usagesafety warning based on smart gas according to some embodiments of thispresent disclosure;

FIG. 3 is an exemplary flowchart illustrating the determining the gasusage threshold according to some embodiments of the present disclosure;

FIG. 4 is an exemplary schematic diagram illustrating the determiningthe target object according to some embodiments of the presentdisclosure; and

FIG. 5 is an exemplary schematic diagram illustrating an evaluationmodel according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The technical solutions of the present disclosure embodiments will bemore clearly described below, and the accompanying drawings need to beconfigured in the description of the embodiments will be brieflydescribed below. Obviously, the drawings in the following descriptionare merely some examples or embodiments of the present disclosure, andwill be applied to these accompanying drawings without having to paycreative labor. Other similar scenarios. Unless obviously obtained fromthe context or the context illustrates otherwise, the same numeral inthe drawings refers to the same structure or operation.

It should be understood that the “system”, “device”, “unit” and/or“module” used herein is a method for distinguishing differentcomponents, elements, components, parts or assemblies of differentlevels. However, if other words may achieve the same purpose, the wordsmay be replaced by other expressions.

As shown in the present disclosure and claims, unless the contextclearly prompts the exception, “a”, “one”, and/or “the” is notspecifically singular form, and the plural form may be included. It willbe further understood that the terms “comprise,” “comprises,” and/or“comprising,” “include,” “includes,” and/or “including,” when used inpresent disclosure, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The flowcharts are used in present disclosure to illustrate theoperations performed by the system according to the embodiment of thepresent disclosure. It should be understood that the front or rearoperation is not necessarily performed in order to accurately. Instead,the operations may be processed in reverse order or simultaneously.Moreover, one or more other operations may be added to the flowcharts.One or more operations may be removed from the flowcharts.

FIG. 1 is a structural diagram illustrating an Internet of Things systemfor gas usage safety warning based on smart gas according to someembodiments of this present disclosure. In some embodiments, theInternet of Things system 100 for gas usage safety warning based onsmart gas may include a smart gas user platform, a smart gas serviceplatform, a smart gas safety management platform, a smart gas householddevice sensing network platform, and a smart gas household device objectplatform that interact in sequence.

The smart gas user platform refers to a user-driven platform thatinteracts with a user. The user may be a gas user, a supervision user,etc. In some embodiments, the smart gas user platform may be configuredas a terminal device. For example, the terminal device may include amobile device, a tablet computer, etc., or any combination thereof. Insome embodiments, the smart gas user platform may feedback informationto the user through the terminal device. For example, the smart gas userplatform may feedback gas usage safety warning information to the userthrough the terminal device (e.g., a display).

In some embodiments, the smart gas user platform may include a gas usersub-platform and a supervision user sub-platform. The gas usersub-platform is aimed at gas users. The gas users may refer to those whoactually use gas, such as consumers. The supervision user sub-platformis aimed at supervision users, which can realize the supervision of theoperation of the whole Internet of things system. The supervision usersare the users of the gas safety supervision department.

In some embodiments, the gas user sub-platform may interact data withthe smart gas service sub-platform of the smart gas service platform.For example, the gas user sub-platform may receive the gas usage safetywarning information uploaded by the smart gas service sub-platform ofthe smart gas service platform, etc. The gas users may obtain the gasusage safety warning information through the gas user sub-platform.

In some embodiments, the supervision user sub-platform may interact datawith a smart supervision service sub-platform of the smart gas serviceplatform. For example, the supervision user sub-platform may issue aquery instruction to the smart supervision service sub-platform of thesmart gas service platform to obtain the monitoring information of thegas system in the corresponding jurisdiction.

The smart gas service platform may be a platform for receiving andtransmitting data and/or information. The smart gas service platform mayinteract data with the smart gas user platform and the smart gas safetymanagement platform. In some embodiments, the smart gas service platformmay upload the gas usage safety warning information to the smart gasuser platform.

In some embodiments, the smart gas service platform may include a smartgas service sub-platform and a smart supervision service sub-platform.The smart gas service platform may interact data with the gas usersub-platform and the supervision user sub-platform respectively,providing the gas users with the gas safety warning information and thesupervision users with the monitoring information of the gas systemrespectively.

In some embodiments, the smart gas service platform may interact withthe smart gas safety management platform. For example, the smart gasservice platform may receive the gas usage safety warning informationuploaded by the smart gas safety management platform.

The smart gas safety management platform may refer to a platform thatcoordinates and collaborates the links and collaboration betweenfunctional platforms. In some embodiments, the smart gas safetymanagement platform may receive the gas usage data uploaded by the smartgas household device sensing network platform, analyze and process it,and upload the processed data to the smart gas user platform through thesmart gas service platform. For example, the smart gas safety managementplatform may determine a gas monitoring object from at least one gasdevice based on the gas usage data; determine initial objects based onthe gas monitoring object and a gas usage threshold; determine a targetobject based on historical gas data of the initial objects; and send gasusage safety warning information to a gas user corresponding to thetarget object.

In some embodiments, the smart gas safety management platform mayinclude a smart gas household safety management sub-platform and a smartgas data center.

The smart gas household safety management sub-platform may be used torealize functions such as intrinsic safety monitoring management,information safety monitoring management, functional safety monitoringmanagement and household safety inspection management. The intrinsicsafety monitoring management may include the monitoring of mechanicalleakage, electrical power consumption (smart control power consumption,communication power consumption, etc.), valve control, and otherexplosion-proof safety. The information safety monitoring management mayinclude safety monitoring of data anomalies, illegal device information,illegal access, etc. The functional safety monitoring management mayinclude safety monitoring of long unused, continuous flow timeout, flowoverload, abnormally high flow, abnormally low flow, low air pressure,strong magnetic interference, low voltage, etc. The household safetyinspection management may include monitoring the use of gas devices inthe household.

The smart gas data center may aggregate and store all the operationaldata of the Internet of Things system 100 for gas usage safety warningbased on the smart gas. The smart gas household safety managementsub-platform interacts with the smart gas data center in bothdirections. In some embodiments, the smart gas data center may receivethe gas usage data uploaded by the smart gas household device sensingnetwork platform and send it to the smart gas household safetymanagement sub-platform for analysis and processing. For example, thesmart gas data center may send the gas usage data to the smart gashousehold safety management sub-platform for analysis and processing,and the smart gas household safety management sub-platform may send theprocessed gas usage data to the smart gas data center.

In some embodiments, the smart gas safety management platform mayinteract with the smart gas service platform and the smart gas householddevice sensing network platform through the smart gas data center.

The smart gas household device sensing network platform may be afunctional platform to manage sensing communications. In someembodiments, the smart gas household device sensing network platform maybe configured as a communication network and gateway for one or more ofnetwork management, protocol management, command management, and dataparsing.

In some embodiments, the smart gas household device sensing networkplatform may interact data with the smart gas safety management platformand the smart gas household device object platform to achieve thefunctions of sensing information sensing communication and controlinformation sensing communication. For example, the smart gas householddevice sensing network platform may receive the gas usage data uploadedby the smart gas household device object platform, or issue aninstruction to obtain the gas usage data to the smart gas householddevice object platform. As another example, the smart gas householddevice sensing network platform may receive an instruction from thesmart gas data center to obtain gas usage data, and upload the gas usagedata to the smart gas data center.

The smart gas household device object platform may refer to thefunctional platform used to obtain sensing information. In someembodiments, the smart gas household device object platform may beconfigured to include at least one gas device. The gas device isconfigured with a unique identification that can be used to control thegas devices deployed in different areas of the city. The gas device isthe relevant device placed at the gas users. The gas device may refer toa device that requires gas for its work. For example, the gas device mayinclude a gas water heater, a gas stove, a dryer, a heating stove, etc.In some embodiments, the smart gas household device object platform mayinteract data with the smart gas household device sensing networkplatform to upload the obtained gas usage data to the smart gashousehold device sensing network platform.

In some embodiments, the smart gas household device object platform mayinclude a fair metering device object sub-platform, a safety monitoringdevice object sub-platform, and a safety valve control device objectsub-platform. The fair metering device object sub-platform may include agas metering meter, etc., the safety monitoring device objectsub-platform may include a gas meter, a thermometer, a barometer, etc.,and the safety valve control device object sub-platform may include avalve control device, etc.

It should be noted that the above description of the system and itscomponents is for descriptive convenience only and does not limit thepresent disclosure to the scope of the embodiments cited. It will beunderstood that it is possible for a person skilled in the art, with anunderstanding of the principle of the system, to make any combination ofthe components or to form sub-systems to connect to other componentswithout departing from this principle. For example, the smart gasservice platform and the smart gas safety management platform may beintegrated in one component. As another example, the various componentsmay share a common storage device, or each component may have its ownstorage device. Variants such as these are within the scope ofprotection of this present disclosure.

FIG. 2 is an exemplary flowchart illustrating a method for gas usagesafety warning based on smart gas according to some embodiments of thispresent disclosure. As shown in FIG. 2 , the process 200 includes thefollowing steps. In some embodiments, the process 200 may be performedby a smart gas safety management platform.

Step 210, based on gas usage data, determining a gas monitoring objectfrom at least one gas device.

The gas usage data may refer to data related to gas usage. In someembodiments, the gas usage data may include gas usage length, gas usageper unit time, usage frequency, and historical usage time periods. Forexample, the gas usage data of a gas cooker may include a gas usage of 1cube per hour, a usage duration of 65 mins, a usage frequency of 3 timesper day, a historical usage time period of 08:00-08:30 and 11:00-12:00,etc.

The gas usage data may be obtained through a gas metering device. Thegas metering device may be used to record gas usage data such as the gasusage of the gas device. The gas metering device may include a gas flowmeter, a gas meter, a thermometer, a barometer, a valve control device,etc. For example, the gas usage data may be obtained through a gasmetering device such as a gas meter or a gas flow meter installed in thehousehold.

The gas monitoring object may refer to the gas device with recent gasusage records. For example, the gas monitoring object may be all the gasmeters in a district that have gas usage records in the last 3 days.

In some embodiments, the smart gas household safety managementsub-platform may determine the gas monitoring object based on the gasusage data of the at least one gas device in a variety of ways. Forexample, the smart gas household safety management sub-platform maydetermine the gas device with gas usage records in the last 3 days asthe gas monitoring object based on the historical usage time period inthe gas usage data of at least one gas device. This present disclosuredoes not limit this.

Step 220, determining initial objects based on the gas monitoring objectand a gas usage threshold.

The gas usage threshold may include a threshold for the safe gas usageunder normal conditions. The gas usage threshold is related to the usagescenario. For example, cooking, bathing, and boiling water scenarios mayhave different gas usage thresholds.

In some embodiments, the gas usage threshold may include a usage lengththreshold and a usage volume threshold. The usage length threshold mayrefer to the length of duration allowed for safe gas usage. For example,the gas usage length allowed for cooking, bathing, boiling water, etc.The usage volume threshold refers to the volume of gas usage per unittime allowed for the safe gas usage. For example, the gas usage volumeallowed for cooking for one hour, bathing for one hour, boiling waterfor one hour, etc.

In some embodiments, the gas usage threshold may be a system defaultvalue, an empirical value, a human preset value, etc., or anycombination thereof, and may be set according to actual needs. The gasusage threshold may also be determined in other ways. For moreinformation about the determining the gas usage threshold, please referto FIG. 3 and its related description.

The initial objects may refer to the gas device that is initiallydetermined to have a possible gas usage safety problem. For example, theinitial objects may be a gas device with a gas usage length of more than12 h. As another example, the initial objects may be a gas device thatuses more gas per unit of time than the normal unit of time. In someembodiments, each initial object may have a suspicion that is used toindicate the degree of suspicion that the initial object has a gas usagesafety problem. For more information about the suspicion, please referto FIG. 4 and its related description.

In some embodiments, the smart gas household safety managementsub-platform may determine a gas monitoring object with gas usage datagreater than the gas usage threshold as the initial objects based on thegas usage data of the gas monitoring object and the corresponding gasusage threshold. By way of example only, the smart gas household safetymanagement sub-platform may determine the gas monitoring object whosegas usage length is greater than the usage length threshold as theinitial object, and the gas monitoring object whose gas usage per unittime is greater than the usage threshold as the initial object.

Step 230, determining a target object based on historical gas data ofthe initial objects.

The historical gas data of the initial objects may refer to thegas-related data of the initial objects during the historical timeperiod. In some embodiments, the historical gas data of the initialobjects may include the basic data, historical usage data, andhistorical time data of the initial objects. The basic data may refer tothe data related to the device state of the initial objects. Thehistorical usage data may refer to the gas usage data of the initialobjects during their historical usage. The historical time data mayrefer to the historical data related to the time when the initialobjects used the gas. For more information about the basic data, thehistorical usage data, and the historical time data, please refer toFIG. 4 and its related description.

The target object may refer to a gas device that has a gas usage safetyproblem as further determined from the initial objects. For example, thetarget object may be the gas device with the highest suspicion rankingamong the initial objects.

In some embodiments, the smart gas household safety managementsub-platform may determine an initial object that meets a presetcondition as the target object. By way of example only, the presetcondition may be that the age of usage is greater than the preset age.For example, one or more of the initial objects whose use age(s) areolder than a preset number of years of usage may be determined as atarget object. As another example, the preset condition may be that thehistorical number of faults is greater than the preset number. Forexample, one or more of the initial objects with a historical number offaults greater than a preset number may be identified as a targetobject.

In some embodiments, the smart gas household safety managementsub-platform may use an evaluation model to process the historical gasdata of the initial objects to determine the suspicions of the initialobjects. Further, the smart gas household safety management sub-platformmay determine the target object based on the suspicion. For moreinformation about the determining the target object, please refer toFIG. 4 and its related description.

Step 240, sending gas usage safety warning information to a gas usercorresponding to the target object.

The gas usage safety warning information may refer to information thatreminds users of gas safety. The gas usage safety warning informationmay be in the form of voice, text, etc. This present disclosure does notlimit this.

In some embodiments, the smart gas household safety managementsub-platform may send the gas usage safety warning information to thesmart gas data center and, through the smart gas service platform, tothe smart gas user platform of the corresponding gas user of the targetobject. For more information about the sending the gas usage safetywarning information, please refer to FIG. 1 and its related description.

In some embodiments, the smart gas user platform may receive feedbackinformation from the gas user corresponding to the target objectregarding the gas usage safety warning information.

The feedback information may refer to the feedback from gas users on theaccuracy of the gas usage safety warning information. The feedbackinformation may include positive feedback information that the gas usagesafety warning information is accurate, i.e., positive feedbackinformation indicates that the gas users confirm the existence of gasusage safety problems. The feedback information may also includenegative feedback information that the feedback gas usage safety warninginformation is inaccurate, i.e., the negative feedback informationindicates that the gas users confirm that there is no gas usage safetyproblem.

The smart gas user platform receives feedback information from gas usersabout gas usage safety warning information in a variety of ways. As anexample, the smart gas user platform receives the gas usage safetywarning information and can remind gas users to provide feedback onwhether the gas usage safety warning information is accurate. Forexample, the smart gas user platform may remind gas users to providefeedback on the accuracy of the gas usage safety warning informationthrough voice, text and vibration. As another example, the gas users maytake the initiative to provide feedback on gas safety problems throughthe smart gas user platform when it does not receive the gas usagesafety warning information. For example, the gas users may proactivelygive feedback on the existence of gas safety problems.

In some embodiments of this present disclosure, by receiving feedbackinformation of the gas users, the accuracy of the gas safety warninginformation can be verified and the actual gas usage of the gas user canbe understood in a timely manner. At the same time, the feedbackinformation of the gas user facilitates the subsequent adjustment of thegas usage threshold and the suspicion threshold to improve the accuracyof determining the target object.

In some embodiments of this present disclosure, the gas usage data isused to identify a gas device with gas usage safety problems and sendthe gas usage safety warning information to the gas user, so that thesafety hazards in the daily use of gas can be investigated and thewarning can be sent to the gas user in a timely manner, whichfacilitates the protection of the gas usage safety of the gas user andreduces the probability of gas safety accidents.

FIG. 3 is an exemplary flowchart illustrating the determining the gasusage threshold according to some embodiments of the present disclosure.As shown in FIG. 3 , the process 300 includes the following steps. Insome embodiments, the process 300 may be performed by the smart gashousehold safety management sub-platform.

Step 310, obtaining environmental data.

The environmental data may refer to environmental data related tocurrent gas usage. In some embodiments, the environmental data mayinclude seasonal information, time-point information, and areainformation.

The seasonal information may refer to information related to current gasusage of the season. For example, the seasonal information may includespring, summer, fall and winter. The seasonal information may alsoinclude the environmental temperature corresponding to the season.

The time-point information may refer to information related to thecurrent gas usage at the time point. The time-point information mayinclude information such as a gas usage date, a start time point, an endtime point and a usage time period.

The area information may refer to the information related to the areawhere the current gas device is located. The area information mayinclude geographical area information of the gas device, administrativedivision area information, etc.

In some embodiments, the smart gas household safety managementsub-platform may obtain the environmental data based on a variety ofways. For example, the smart gas household safety managementsub-platform may obtain the environmental data stored in the smart gasdata center. Specifically, the seasonal information may be obtained fromthe smart gas data center based on the network, and the time-pointinformation and the area information may be obtained from the smart gashousehold device object platform based on the smart gas data center. Asanother example, the smart gas household safety management sub-platformmay obtain the environmental in a manual way. Specifically, theenvironmental data input by the user terminal may be obtained.

In some embodiments, the smart gas household safety managementsub-platform may determine a gas usage threshold based on theenvironmental data by using vector matching. The process of determiningthe gas usage threshold by using vector matching will be described bythe following steps 320-step 350.

Step 320, constructing a gas environment vector based on theenvironmental data.

The gas environment vector may refer to a vector constructed fromenvironmental data related to gas usage. For example, the gasenvironment vector=(a, b, c) may be constructed, where a may representseasonal information in the environmental data, b may representtime-point information, and c may represent area information.Exemplarily, the gas environment vector=(winter, 9:00-10:00, CommunityA) may indicate that a particular gas device located in Community A isin use during the winter, with usage times of 9:00-10:00.

In some embodiments, the smart gas household safety managementsub-platform may construct a gas environment vector by any feasiblealgorithm based on the environmental data. For example, a vector may beextracted by a model. As another example, the gas environment vector maybe constructed manually.

Step 330, constructing a reference database, wherein the referencedatabase includes a reference vector and gas usage data corresponding tothe reference vector.

The reference database consists of multiple reference vectors and theircorresponding gas usage data.

The reference vector may refer to a vector constructed based onhistorical environmental data. The historical environmental data mayinclude environmental data for a certain historical time period. Forexample, the historical environmental data may be the environmental datacorresponding to the gas usage in a historical month.

The gas usage data corresponding to the reference vector may include theusage length and usage volume when the reference vector corresponds tothe gas usage. For example, the reference vector=(autumn, 18:00-19:00,Community A), indicates that a gas device located in Community A is usedin autumn, with the usage time of 18:00-19:00, and its corresponding gasusage data may include the gas usage length of 1 hour, the gas usagevolume of 1 cubic meter per hour, etc.

In some embodiments, the smart gas household safety managementsub-platform may determine the reference vector based on the historicalenvironmental data in a variety of ways. For example, the historical gasenvironment vector corresponding to the historical environment data maybe vector clustered based on a clustering algorithm (e.g., K-meanalgorithm) to obtain at least one cluster center. Further, thehistorical gas environment vector corresponding to the at least onecluster center may be used as a reference vector, and the gas usage datacorresponding to this historical gas environment vector may be used asthe gas usage data corresponding to the reference vector. This presentdisclosure does not limit this.

Step 340, determining at least one target reference vector by usingvector matching based on the gas environment vector and the referencedatabase.

The target reference vector may refer to a reference vector that matchesthe gas environment vector. For more information about the targetreference vector, please refer to the reference vector above.

In some embodiments, the smart gas household safety managementsub-platform may determine at least one target reference vector by usingvector matching in various feasible ways based on the gas environmentvector and the reference database. For example, a similarity between thereference vector in the reference vector library and the gas environmentvector may be calculated, and the reference vector with a similaritygreater than a similarity threshold is used as the target referencevector. The similarity may be expressed based on a vector distance, andthe smaller the vector distance is, the higher the similarity is. Thevector distance may be expressed based on cosine distance, Euclideandistance, or Hamming distance, etc.

Step 350, determining the gas usage threshold based on gas usage datacorresponding to the at least one target reference vector.

In some embodiments, the smart gas household safety managementsub-platform may determine a usage length threshold based on the usagelength in the gas usage data corresponding to the at least one targetreference vector, and a usage volume threshold based on the usage volumein the gas usage data corresponding to the at least one target referencevector. For example, an average value of the usage length in the gasusage data corresponding to at least one target reference vector may beused as the usage length threshold, and an average value of the usagevolume in the gas usage data corresponding to at least one targetreference vector may be used as the usage volume threshold. As anotherexample, the smart gas household safety management sub-platform may usethe gas usage data corresponding to the target reference vector with thehighest similarity as the gas usage threshold.

In some embodiments of this present disclosure, determining a gas usagethreshold based on the environmental data can fully consider the impactof different seasons, time points and areas on gas usage, and thus canmake adaptive adjustments to the gas usage threshold based on differentenvironments, making the determination of the gas usage threshold morereasonable and accurate.

In some embodiments, the smart gas household safety managementsub-platform may adjust the gas usage threshold based on feedbackinformation from gas users or the number of gas usage safety warnings.

The feedback information from the gas users refers to the feedback fromthe gas users about the gas safety warning information. For moreinformation about the feedback information, please refer to FIG. 2 andits related description.

The number of gas usage safety warnings may refer to the number of timesa gas user receives gas usage safety warning information within acertain time period. For example, the number of gas usage safetywarnings may be the number of gas usage safety warning informationreceived in a historical month.

In some embodiments, the smart gas household safety managementsub-platform may adjust the gas usage threshold based on the feedbackinformation from gas users. For example, when the number of inaccurategas usage safety warnings (subsequently referred to as “invalidwarnings”) received from users is too high, the gas usage threshold maybe increased. If the number of invalid warnings caused by the usagelength is too high, the usage length threshold may be increased; if thenumber of invalid warnings caused by usage volume is too high, the usagevolume threshold may be adjusted. As another example, if a user givesfeedback on gas safety problems but the gas usage safety warninginformation is not timely or has not been issued, the gas usagethreshold may be lowered.

In some embodiments, the smart gas household safety managementsub-platform may adjust the gas usage threshold based on the number ofgas usage safety warnings. For example, a first threshold of the numberof gas users receiving gas usage safety warnings over a period of timemay be preset. When the number of gas usage safety warnings by gas usersover a period of time exceeds the first threshold, it can furtherdetermine whether the number of times that the gas users feedback gasusage safety warning information being inaccurate exceeds a secondthreshold or a third threshold. The second threshold is the thresholdthat corresponds to the feedback when the gas usage safety warninginformation caused by the usage length is inaccurate. The thirdthreshold is the threshold when the feedback of the gas usage safetywarning information caused by the usage volume is inaccurate. If thenumber of invalid warnings exceeds the second threshold, the usagelength threshold may be adjusted upwards accordingly; if the number ofinvalid warnings exceeds the third threshold, the usage volume thresholdmay be adjusted accordingly. As another example, if a gas user does notreceive gas safety warning information during the gas usage process, butthe user feeds back that there is a gas usage safety problem, the gasusage threshold may be adjusted downward accordingly. The adjustmentamount of the gas usage threshold may be determined by human beings. Thefirst threshold is greater than the second threshold or the thirdthreshold. The first threshold, the second threshold and the thirdthreshold may be a system default value, an empirical value, a humanpreset value, or any combination thereof, or set according to actualneeds, and this present disclosure does not limit this.

In some embodiments of this present disclosure, the gas usage thresholdis determined based on the user's feedback information and the number ofwarnings, and the corresponding gas usage threshold can be flexiblyadjusted for the gas usage of different users, thus making the gas usagesafety warning information more consistent with the actual gas usage ofdifferent users.

In some embodiments, the smart gas household safety managementsub-platform may also adjust the gas usage threshold based on the numberof user feedbacks and suspicion of the gas user.

The number of user feedback may refer to the number of times a gas usergives feedback on whether the gas usage safety warning information isinaccurate over a period of time. For example, the number of userfeedbacks may be the number of times in the past month that a gas userhas fed back gas usage safety warning information as inaccurate. Thenumber of user feedbacks may include the number of positive feedbackswhere the feedbacks are accurate and the number of negative feedbackswhere the feedbacks are inaccurate.

The suspicion refers to the degree of suspicion that there is a gasusage safety problem with the gas device when the gas is used. Thehigher the suspicion is, the higher the likelihood that the gas devicehas a gas usage safety problem, and the higher the likelihood that a gasusage safety warning will be required. The suspicion may be expressed invarious ways. For example, the suspicion may be expressed as a numericalrepresentation from 0 to 100. In some embodiments, the suspicion levelmay be obtained based on an evaluation model. For more information aboutthe suspicion and the evaluation model, please refer to FIG. 4 and itsrelated description.

In some embodiments, the smart gas household safety managementsub-platform may adjust the gas usage thresholds in various feasibleways based on the number of user feedbacks and suspicion of gas users.For example, when the number of negative feedbacks from gas users is lowand the suspicion is less than a suspicion threshold, the gas usagethreshold may be adjusted upwards. When the number of positive feedbackfrom gas users is high and the suspicion is greater than the suspicionthreshold, the gas usage threshold may be adjusted downward. For moreinformation about the suspicion threshold, please refer to FIG. 4 andits related description.

In some embodiments of this present disclosure, adjusting the gas usagethreshold based on the number and suspicion of the user's historicalfeedback information can make the gas usage threshold more consistentwith the user's gas usage, improve the accuracy of the warninginformation, and reduce the risk of untimely warnings.

FIG. 4 is an exemplary schematic diagram illustrating the determiningthe target object according to some embodiments of the presentdisclosure.

In some embodiments, the smart gas household safety managementsub-platform may use an evaluation model to determine the suspicions ofthe initial objects, as well as to determine the target object based onthe suspicions.

The evaluation model may be used to determine the suspicions of theinitial objects. The evaluation model is a machine learning model, andin some embodiments, the evaluation model may include any one or acombination of various feasible models such as a Recurrent NeuralNetwork (RNN) model, a Deep Neural Network (DNN) model, a ConvolutionalNeural Network (CNN) model, etc.

As a specific example, as shown in FIG. 4 , the smart gas householdsafety management sub-platform uses an evaluation model 420 to determinethe suspicion 430 s of the initial objects. An input to the evaluationmodel 420 is historical gas data 410 of the initial objects and anoutput is the suspicions 430 of the initial objects.

In some embodiments, the evaluation model may include multipleprocessing layers. FIG. 5 is an exemplary schematic diagram illustratingan evaluation model according to some embodiments of the presentdisclosure. As shown in FIG. 5 , the evaluation model 420 may include anobject feature extraction layer 421, a usage feature extraction layer423, a time feature extraction layer 425, and an evaluation layer 427.

The object feature extraction layer 421 is used to process basic data411 of the initial objects and determine the basic feature vector 422.The object feature extraction layer 421 may be constructed based on CNN,RNN and other models. The basic data may refer to the data related tothe device state of the initial objects. For example, the basic data ofthe initial objects may include the safety performance of the gasdevice, energy consumption, usage age, and maintenance within the lastyear. The basic feature vector is a feature vector constructed based onthe underlying data of the initial objects. For example, the basisfeature vector may be represented as =(a, b, c, d), where each elementmay represent one type of basis data.

The usage feature extraction layer 423 is used to process historicalusage data 412 of the initial objects to determine the usage featurevector 424. The usage feature extraction layer may be CNN, RNN and otherstructures. The historical usage data of the initial objects may referto data such as the usage length of the gas device and the usage volumeper unit time. The usage feature vector is a feature vector constructedbased on the historical usage data of the initial objects. For example,the usage feature vector may be represented as =(a, b), where a mayrepresent the usage length and b may represent the usage volume per unittime.

The time feature extraction layer 425 is used to process the historicaltime data 413 of the initial objects and determine the time featurevector 426. The time feature extraction layer may be structures such asCNN, RNN, etc. The historical time data of the gas usage of the initialobjects may refer to the data of the start time point of the gas device,the end time point of the usage, the number of times of usage in a day,the number of times of usage in a week, etc. The time feature vector isa feature vector constructed based on the historical time data of theinitial objects. For example, the time feature vector may be representedas =(a, b, c . . . ), where each element may represent one type ofhistorical time data.

The evaluation layer 427 is used to process the basic feature vector422, the usage feature vector 424, and the time feature vector 426 todetermine the suspicions 430 of the initial objects. The evaluationlayer may be a structure such as Long Short-Term Memory (LSTM) network.

In some embodiments, the evaluation model may be obtained by jointtraining. The training sample used for joint training may include thebasic data of the initial objects of the sample, historical usage dataand historical time data, and the training sample is derived fromhistorical data. The label of the training sample is the actualsuspicion of the initial object of the sample. The label may be derivedfrom historical data or labeled manually. By way of example only, ahuman may make an evaluation of the current suspicion of historical databased on experience to determine the actual suspicion based onhistorical data. For example, the suspicion may be any value between 0and 1, where 0 indicates that there is no gas usage safety problem(i.e., no abnormality has occurred or is likely to occur), 1 indicatesthat there is a gas usage safety problem (i.e., an abnormality hasoccurred), and other values indicate that there may be a gas usagesafety problem (i.e., no abnormality has occurred but may occur), andthe size of the value indicates the suspicion that there is a gas usagesafety problem.

The exemplary joint training process includes: inputting the basic data,the historical usage data and the historical time data of the sampleinitial objects into the initial object feature extraction layer, theinitial usage feature extraction layer and the initial time featureextraction layer, respectively, to obtain the basic feature vectoroutputted by the initial object feature extraction layer, the usagefeature vector outputted by the initial usage feature extraction layerand the time feature vector outputted by the initial time featureextraction layer, and then inputting the basic feature vector, the usagefeature vector and the time feature vector into the initial evaluationlayer to obtain the suspicion of the sample initial object outputted bythe evaluation layer. Based on the output results of the label and theinitial evaluation layer, the loss function is established, theparameters of the initial object feature extraction layer, the initialusage feature extraction layer, the initial time feature extractionlayer and the initial evaluation layer are updated, the model trainingis completed when the loss function meets the preset condition, and thetrained evaluation model is obtained. The preset condition may be thatthe loss function converges, the number of iterations reaches athreshold number of iterations, etc.

In some embodiments of this present disclosure, the suspicion isdetermined by the evaluation model, which can fully consider theinfluence of the basic data and usage of the gas device on thesuspicion, reduce the calculation of the suspicion, and improve theefficiency and accuracy of the calculation of the suspicion. Moreover,obtaining the trained evaluation model by joint training is helpful tosolve the problem of difficult to obtain the label when training objectfeature extraction layer, usage feature extraction layer and timefeature extraction layer separately in some cases. At the same time,obtaining the trained evaluation model based on joint training can makethe evaluation model better to get the doubtfulness.

In some embodiments, the smart gas household safety managementsub-platform may determine a target object based on the suspicion. Forexample, the smart gas household safety management sub-platform maydetermine the initial objects with the top suspicion ranking as thetarget object based on the ranking of suspicion from largest tosmallest. Exemplarily, the initial object in the top 3 or top 20% of theranking may be determined as the target object.

In some embodiments, the smart gas household safety managementsub-platform may determine the target object based on a suspicionthreshold. As a specific example, as shown in FIG. 4 , the smart gashousehold safety management sub-platform may determine whether thesuspicions 430 are greater than a suspicion threshold, and identify theinitial objects whose suspicions 430 are greater than the suspicionthreshold as the target object 440.

The suspicion threshold is the threshold condition used to determine thetarget object. For example, the suspicion threshold may be expressed bya specific value.

The suspicion threshold may be a system default value, an empiricalvalue, a human preset value, or any combination thereof, and can be setaccording to actual needs. In some embodiments, the suspicion thresholdmay be determined based on historical data. For example, an average ofthe historically used suspicion threshold may be used as the currentsuspicion threshold.

In some embodiments, the suspicion threshold may also be adjusted basedon feedback information from gas users. For example, when gas users givefeedback on the number of inaccurate gas usage safety warnings, thesuspicion threshold may be manually adjusted. If the content of thefeedback information is that there is a gas usage safety problem but thegas usage safety warning is not timely, the suspicion threshold may beadjusted downward; if the content of the feedback information is thatthere is no gas usage safety problem but gas usage safety warninginformation is received, the suspicion threshold may be adjustedupwards.

In some embodiments of this specification, the target object isdetermined based on the suspicion threshold, which can make thedetermination of the target object more reasonable and the sending ofgas usage safety warning information more targeted, avoiding the impacton users with normal gas usage.

The basic concepts have been described above, apparently, in detail, aswill be described above, and does not constitute a limitation of thespecification. Although there is no clear explanation here, thoseskilled in the art may make various modifications, improvements, andmodifications of present disclosure. This type of modification,improvement, and corrections are recommended in present disclosure, sothis class is modified, improved, and the amendment remains in thespirit and scope of the exemplary embodiment of the present disclosure.

At the same time, present disclosure uses specific words to describe theembodiments of the present disclosure. As “one embodiment”, “anembodiment”, and/or “some embodiments” means a certain feature,structure, or characteristic of at least one embodiment of the presentdisclosure. Therefore, it is emphasized and should be appreciated thattwo or more references to “an embodiment” or “one embodiment” or “analternative embodiment” in various parts of present disclosure are notnecessarily all referring to the same embodiment. Further, certainfeatures, structures, or features of one or more embodiments of thepresent disclosure may be combined.

Moreover, unless the claims are clearly stated, the sequence of thepresent disclosure, the use of the digital letters, or the use of othernames, is not configured to define the order of the present disclosureprocesses and methods. Although some examples of the invention currentlyconsidered useful in the present invention are discussed in the abovedisclosure, it should be understood that the details of this class willonly be described, and the appended claims are not limited to thedisclosure embodiments. The requirements are designed to cover allmodifications and equivalents combined with the substance and range ofthe present disclosure. For example, although the implementation ofvarious components described above may be embodied in a hardware device,it may also be implemented as a software only solution, e.g., aninstallation on an existing server or mobile device.

Similarly, it should be noted that in order to simplify the expressiondisclosed in the present disclosure and help the understanding of one ormore invention embodiments, in the previous description of theembodiments of the present disclosure, a variety of features aresometimes combined into one embodiment, drawings or description thereof.However, this disclosure method does not mean that the characteristicsrequired by the object of the present disclosure are more than thecharacteristics mentioned in the claims. Rather, claimed subject mattermay lie in less than all features of a single foregoing disclosedembodiment.

In some embodiments, numbers expressing quantities of ingredients,properties, and so forth, configured to describe and claim certainembodiments of the application are to be understood as being modified insome instances by the term “about,” “approximate,” or “substantially”.Unless otherwise stated, “approximately”, “approximately” or“substantially” indicates that the number is allowed to vary by ±20%.Accordingly, in some embodiments, the numerical parameters used in thespecification and claims are approximate values, and the approximatevalues may be changed according to characteristics required byindividual embodiments. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Although thenumerical domains and parameters used in the present disclosure areconfigured to confirm its range breadth, in the specific embodiment, thesettings of such values are as accurately as possible within thefeasible range.

For each patent, patent application, patent application publication andother materials referenced by the present disclosure, such as articles,books, instructions, publications, documentation, etc., herebyincorporated herein by reference. Except for the application historydocuments that are inconsistent with or conflict with the contents ofthe present disclosure, and the documents that limit the widest range ofclaims in the present disclosure (currently or later attached to thepresent disclosure). It should be noted that if a description,definition, and/or terms in the subsequent material of the presentdisclosure are inconsistent or conflicted with the content described inthe present disclosure, the use of description, definition, and/or termsin this manual shall prevail.

Finally, it should be understood that the embodiments described hereinare only configured to illustrate the principles of the embodiments ofthe present disclosure. Other deformations may also belong to the scopeof the present disclosure. Thus, as an example, not limited, thealternative configuration of the present disclosure embodiment may beconsistent with the teachings of the present disclosure. Accordingly,the embodiments of the present disclosure are not limited to theembodiments of the present disclosure clearly described and described.

What is claimed is:
 1. A method for gas usage monitoring and warningbased on smart gas, which is performed by a smart gas safety managementplatform of an Internet of Things system for gas usage monitoring andwarning based on smart gas, comprising: obtaining gas usage data from atleast one gas device through a smart gas household device sensingnetwork platform of the Internet of Things system and sending the gasusage data to the smart gas safety management platform, wherein the atleast one gas device is configured in a smart gas household deviceobject platform of the Internet of Things system; based on the gas usagedata, determining a gas monitoring object from the at least one gasdevice; determining initial objects based on the gas monitoring objectand a gas usage threshold; processing historical gas data of the initialobjects by using an evaluation model to determine suspicions of theinitial objects, wherein the evaluation model is a machine learningmodel and obtained through joint training; determining a target objectbased on the suspicions; and sending gas usage safety warninginformation to a gas user corresponding to the target object.
 2. Themethod of claim 1, wherein the Internet of Things system for gas usagemonitoring and warning based on smart gas further includes a smart gasuser platform and a smart gas service platform interacted in sequence,wherein the smart gas safety management platform includes a smart gashousehold safety management sub-platform and a smart gas data center,and the method further includes: by the smart gas household safetymanagement sub-platform, sending the gas safety warning information tothe smart gas data center and to the smart gas user platform of the gasuser corresponding to the target object via the smart gas serviceplatform.
 3. The method of claim 1, wherein the determining the gasusage threshold includes: obtaining environmental data, wherein theenvironmental data includes seasonal information, time-pointinformation, and area information; and based on the environmental data,determining the gas usage threshold by using vector matching.
 4. Themethod of claim 3, wherein the gas usage threshold includes a usagelength threshold and a usage volume threshold, and the determining thegas usage threshold by using vector matching based on the environmentaldata includes: constructing a gas environment vector based on theenvironmental data; constructing a reference database, wherein thereference database includes a reference vector and gas usage datacorresponding to the reference vector, the reference vector beingconstructed based on historical environmental data; determining at leastone target reference vector by using vector matching based on the gasenvironment vector and the reference database; and determining the gasusage threshold based on gas usage data corresponding to the at leastone target reference vector.
 5. The method based of claim 3, wherein thedetermining the gas usage threshold further includes: adjusting the gasusage threshold based on feedback information of the gas user or a countof gas usage safety warnings.
 6. The method of claim 3, wherein thedetermining the gas usage threshold further includes: adjusting the gasusage threshold based on a count of user feedback and a suspicion of thegas user, wherein the count of user feedback is a count of feedback ofthe gas user on whether the gas usage safety warning information isaccurate.
 7. The method of claim 1, wherein the evaluation modelincludes an object feature extraction layer, a usage feature extractionlayer, a time feature extraction layer and an evaluation layer; theobject feature extraction layer is used to process basic data of theinitial objects to determine a basic feature vector, and the basic dataincludes data related to a device state of the initial objects; theusage feature extraction layer is used to process historical usage dataof the initial objects to determine a usage feature vector, and thehistorical usage data includes a usage length of the gas device and ausage volume per unit time; the time feature extraction layer is used toprocess historical time data of the initial objects using gas todetermine a time feature vector, and the historical time data includesdata of a start time point of the gas device, an end time point ofusage, a count of times of usage in a day, or a count of times of usagein a week; and the evaluation layer is used to process the basic featurevector, the usage feature vector and the time feature vector todetermine the suspicions of the initial objects.
 8. The method of claim1, wherein the determining the target object based on the suspicionsincludes: determining an initial object with a suspicion greater than asuspicion threshold as the target object.
 9. The method of claim 8,wherein the suspicious threshold is determined based on feedbackinformation from the gas user.
 10. The method of claim 1, wherein themethod further includes: receiving feedback information for the gasusage safety warning information from the gas user corresponding to thetarget object via the smart gas user platform.
 11. An Internet of Thingssystem for gas usage monitoring and warning based on smart gas, whereinthe Internet of Things system comprises a smart gas user platform, asmart gas service platform, a smart gas safety management platform, asmart gas household device sensing network platform and a smart gashousehold device object platform interacted in sequence, wherein thesmart gas safety management platform includes a smart gas householdsafety management sub-platform and a smart gas data center, the smartgas data center is configured to: obtain gas usage data from at leastone gas device through the smart gas household device sensing networkplatform and send the gas usage data to the smart gas household safetymanagement sub-platform, wherein the at least one gas device isconfigured in the smart gas household device object platform; and thesmart gas household safety management sub-platform is configured to:based on the gas usage data, determine a gas monitoring object from theat least one gas device; determine initial objects based on the gasmonitoring object and a gas usage threshold; process historical gas dataof the initial objects by using an evaluation model to determinesuspicions of the initial objects, wherein the evaluation model is amachine learning model and obtained through joint training; determine atarget object based on the suspicions; and send gas safety warninginformation to the smart gas data center and to the smart gas userplatform of the gas user corresponding to the target object via thesmart gas service platform.
 12. The Internet of Things system of claim11, wherein the smart gas household safety management sub-platform isconfigured to: obtain environmental data, wherein the environmental dataincludes seasonal information, time-point information, and areainformation; and based on the environmental data, determine the gasusage threshold by using vector matching.
 13. The Internet of Thingssystem of claim 12, wherein the gas usage threshold includes a usagelength threshold and a usage volume threshold; to determine the gasusage threshold by using vector matching based on the environmentaldata, the smart gas household safety management sub-platform is furtherconfigured to: construct a gas environment vector based on theenvironmental data; construct a reference database, wherein thereference database includes a reference vector and gas usage datacorresponding to the reference vector, the reference vector beingconstructed based on historical environmental data; determine at leastone target reference vector by using vector matching based on the gasenvironment vector and the reference database; and determine the gasusage threshold based on gas usage data corresponding to the at leastone target reference vector.
 14. The Internet of Things system of claim12, wherein the smart gas household safety management sub-platform isfurther configured to: adjust the gas usage threshold based on feedbackinformation of the gas user or a count of gas usage safety warnings. 15.The Internet of Things system of claim 12, wherein the smart gashousehold safety management sub-platform is further configured to:adjust the gas usage threshold based on a count of user feedback and asuspicion of the gas user, wherein the count of user feedback is a countof feedback of the gas user on whether the gas usage safety warninginformation is accurate.
 16. The Internet of Things system of claim 11,wherein the evaluation model includes an object feature extractionlayer, a usage feature extraction layer, a time feature extraction layerand an evaluation layer; the object feature extraction layer is used toprocess basic data of the initial objects to determine a basic featurevector, and the basic data includes data related to a device state ofthe initial objects; the usage feature extraction layer is used toprocess the historical usage data of the initial objects to determine ausage feature vector, and the historical usage data includes a usagelength of the gas device and a usage volume per unit time; the timefeature extraction layer is used to process historical time data of theinitial objects using gas to determine a time feature vector, and thehistorical time data includes data of a start time point of the gasdevice, an end time point of usage, a count of times of usage in a day,or a count of times of usage in a week; and the evaluation layer is usedto process the basic feature vector, the usage feature vector and thetime feature vector to determine the suspicions of the initial objects.17. The Internet of Things system of claim 16, wherein in order todetermine the target object based on the suspicions, the smart gashousehold safety management sub-platform is further configured to:determine an initial object with a suspicion greater than a suspicionthreshold as the target object.
 18. The Internet of Things system ofclaim 17, wherein the suspicious threshold is determined based onfeedback information from the gas user.
 19. The Internet of Thingssystem of claim 11, wherein the smart gas household safety managementsub-platform is further configured to: receive feedback information forthe gas usage safety warning information from the gas user correspondingto the target object via the smart gas user platform.
 20. Anon-transitory computer-readable storage medium, comprising a set ofinstructions, wherein when executed by a processor, the method for gasusage monitoring and warning based the smart gas of claim 1 isimplemented.